Difference between revisions of "2014 Project Week:GraphCutsLASegmentationModule"

Revision as of 18:29, 18 December 2013

Home < 2014 Project Week:GraphCutsLASegmentationModule

Gopalkrishna Veni (SCI Institute, University of Utah)

Salma Bengali (CARMA center, University of Utah)

Josh Cates (CARMA center, University of Utah)

Ross Whitaker(SCI Institute, University of Utah)

Develop a Slicer module that automatically segments the left atrial wall from a given LGE-MRI image.

Involves Bayesian formulation with Markov random field prior within a nested-layer 3D mesh which leads to surface-net problem [Veni et al, IPMI 2013].
Solved by using VCEnet strategy and graph-cuts [Wu and Chen, 2002].
Uses training strategy in order to generate model shapes as well as to compute costs at each mesh point.

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 <div style="width: 27%; float: left; padding-right: 3%;">
 <h3>Objective</h3>
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+* Develop a Slicer module that automatically segments the left atrial wall from a given LGE-MRI image.
 </div>
 <div style="width: 27%; float: left; padding-right: 3%;">
 <h3>Approach, Plan</h3>
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+* Involves Bayesian formulation with Markov random field prior within a nested-layer 3D mesh which leads to surface-net problem [Veni ''et al'', IPMI 2013].
+* Solved by using VCEnet strategy and graph-cuts [Wu and Chen, 2002].
+* Uses training strategy in order to generate model shapes as well as to compute costs at each mesh point.
 </div>
 <div style="width: 27%; float: left; padding-right: 3%;">
 <h3>Progress</h3>
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+* Module is now available in the Slicer. It could be downloaded from
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 </div>